ABSTRACT
Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.
Subject(s)
Chromatin , Genome , Humans , Animals , Mice , Chromatin/genetics , MultiomicsABSTRACT
HOTAIR is a 2.2-kb long noncoding RNA (lncRNA) whose dysregulation has been linked to oncogenesis, defects in pattern formation during early development, and irregularities during the process of epithelial-to-mesenchymal transition (EMT). However, the oncogenic transformation determined by HOTAIR in vivo and its impact on chromatin dynamics are incompletely understood. Here, we generate a transgenic mouse model with doxycycline-inducible expression of human HOTAIR in the context of the MMTV-PyMT breast cancer-prone background to systematically interrogate the cellular mechanisms by which human HOTAIR lncRNA acts to promote breast cancer progression. We show that sustained high levels of HOTAIR over time increased breast metastatic capacity and invasiveness in breast cancer cells, promoting migration and subsequent metastasis to the lung. Subsequent withdrawal of HOTAIR overexpression reverted the metastatic phenotype, indicating oncogenic lncRNA addiction. Furthermore, HOTAIR overexpression altered both the cellular transcriptome and chromatin accessibility landscape of multiple metastasis-associated genes and promoted EMT. These alterations are abrogated within several cell cycles after HOTAIR expression is reverted to basal levels, indicating an erasable lncRNA-associated epigenetic memory. These results suggest that a continual role for HOTAIR in programming a metastatic gene regulatory program. Targeting HOTAIR lncRNA may potentially serve as a therapeutic strategy to ameliorate breast cancer progression.
Subject(s)
Breast Neoplasms , RNA, Long Noncoding , Animals , Female , Humans , Mice , Breast Neoplasms/genetics , Breast Neoplasms/pathology , Cell Line, Tumor , Cell Proliferation , Chromatin , Gene Expression Regulation , Gene Expression Regulation, Neoplastic , Mice, Transgenic , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , Lung Neoplasms/secondaryABSTRACT
The quantification of developmental potential is critical for determining developmental stages and identifying essential molecular signatures in single-cell studies. Here, we present FitDevo, a novel method for inferring developmental potential using scRNA-seq data. The main idea of FitDevo is first to generate sample-specific gene weight (SSGW) and then infer developmental potential by calculating the correlation between SSGW and gene expression. SSGW is generated using a generalized linear model that combines sample-specific information and gene weight learned from a training dataset covering scRNA-seq data of 17 previously published datasets. We have rigorously validated FitDevo's effectiveness using a testing dataset with scRNA-seq data from 28 existing datasets and have also demonstrated its superiority over current methods. Furthermore, FitDevo's broad application scope has been illustrated using three practical scenarios: deconvolution analysis of epidermis, spatial transcriptomic data analysis of hearts and intestines, and developmental potential analysis of breast cancer. The source code and related data are available at https://github.com/jumphone/fitdevo.
